LeetCode42 Trapping Rain Water
题目:
Given n non-negative integers representing an elevation map where the width of each bar is 1, compute how much water it is able to trap after raining.
For example,
Given [0,1,0,2,1,0,1,3,2,1,2,1]
, return 6
.
The above elevation map is represented by array [0,1,0,2,1,0,1,3,2,1,2,1]. In this case, 6 units of rain water (blue section) are being trapped. (Hard)
分析:
首先考虑对每个位置向左向右找到最小值,然后与本位置的值比较添加存水结果,可以做出来,时间复杂度O(n^2);
考虑利用空间优化时间,开两个数组,分别存当前位置向左的最小值和向右的最小值。
一次从左往右遍历更新leftHeight数组, 一次从右向左遍历更新rightHeight数组,最后一次遍历算利用上述方法算result,只不过这次可以直接从数组里读左右最小值结果。
代码:
class Solution {
public:
int trap(vector<int>& height) {
int leftMax[height.size()];
leftMax[] = ;
for (int i = ; i < height.size(); ++i) {
leftMax[i] = max(height[i - ], leftMax[i - ]);
}
int rightMax[height.size()];
rightMax[height.size() - ] = ;
for (int i = height.size() - ; i >= ; --i) {
rightMax[i] = max(height[i + ], rightMax[i + ]);
}
int result = ;
for (int i = ; i < height.size(); ++i) {
if (min(leftMax[i], rightMax[i]) - height[i] > ) {
result += (min(leftMax[i], rightMax[i]) - height[i]);
}
}
return result;
}
};
上述方法将时间复杂度优化到了O(n),但利用了额外的空间,空间复杂度也提高到了O(n)。
进一步优化,可以考虑Two pointers的思路。
两根指针分别指向头和尾,并维护两个值,表示从左向右到p1的最大值leftHeight和从右向左到p2的最大值RightHeight。
两个最大值中较小的向中间移动,遇到更大的值更新leftHeight或rightHeight,遇到较小的值更新result。
这样可以做到时间复杂度O(n),空间复杂度O(1)。
代码:
class Solution {
public:
int trap(vector<int>& height) {
if (height.size() < ) {
return ;
}
int left = , right = height.size() - ;
int leftHeight = height[], rightHeight = height[height.size() - ];
int result = ;
while (left < right) {
if (leftHeight <= rightHeight) {
left++;
if (height[left] < leftHeight) {
result += (leftHeight - height[left]);
}
else {
leftHeight = height[left];
}
}
else {
right--;
if (height[right] < rightHeight) {
result += (rightHeight - height[right]);
}
else {
rightHeight = height[right];
}
}
}
return result;
}
};
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